IDEAS home Printed from https://ideas.repec.org/p/hhs/umnees/0535.html
   My bibliography  Save this paper

ASYMMETRIES IN CONDITIONAL MEAN AND VARIANCE: MODELLING STOCK RETURNS BY asMA-asQGARCH

Author

Listed:
  • Brännäs, Kurt

    () (Department of Economics, Umeå University)

  • de Gooijer, Jan G.

    () (Department of Quantitative Economics)

Abstract

The asymmetric moving average model (asMA) is extended to allow for asymmetric quadratic conditional heteroskedasticity (asQGARCH). The asymmetric parametrization of the conditional variance encompasses the quadratic GARCH model of Sentana (1995). We introduce a framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. Some of the new model's moment properties are also derived. Empirical results are given for the daily returns of the composite index of the New York Stock Exchange. There is strong evidence of asymmetry in both the conditional mean and conditional variance functions. In a genuine out-of-sample forecasting experiment the performance of the best fitted asMA-asQGARCH model is compared to pure asMA and no-change forecasts. This is done both in terms of conditional mean forecasting as well in terms of risk forecasting.

Suggested Citation

  • Brännäs, Kurt & de Gooijer, Jan G., 2000. "ASYMMETRIES IN CONDITIONAL MEAN AND VARIANCE: MODELLING STOCK RETURNS BY asMA-asQGARCH," Umeå Economic Studies 535, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0535
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Modelling economic high-frequency time series with STAR-STGARCH models," SSE/EFI Working Paper Series in Economics and Finance 291, Stockholm School of Economics.
    3. LeBaron, Blake, 1992. "Some Relations between Volatility and Serial Correlations in Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 65(2), pages 199-219, April.
    4. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(04), pages 465-487, December.
    5. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
    6. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
    7. Li, C W & Li, W K, 1996. "On a Double-Threshold Autoregressive Heteroscedastic Time Series Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(3), pages 253-274, May-June.
    8. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    9. Enrique Sentana, 1995. "Quadratic ARCH Models," Review of Economic Studies, Oxford University Press, vol. 62(4), pages 639-661.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    12. Kurt Brännäs & Henry Ohlsson, 1999. "Asymmetric Time Series and Temporal Aggregation," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 341-344, May.
    13. Gregory Koutmos, 1999. "Asymmetric index stock returns: evidence from the G-7," Applied Economics Letters, Taylor & Francis Journals, vol. 6(12), pages 817-820.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taştan, Hüseyin, 2011. "Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry," MPRA Paper 34302, University Library of Munich, Germany.
    2. Hua, Zhongsheng & Zhang, Bin, 2008. "Improving density forecast by modeling asymmetric features: An application to S&P500 returns," European Journal of Operational Research, Elsevier, vol. 185(2), pages 716-725, March.
    3. Kurt Brannas & Niklas Nordman, 2003. "Conditional skewness modelling for stock returns," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 725-728.
    4. Srikanta Kundu & Nityananda Sarkar, 2016. "Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study," International Econometric Review (IER), Econometric Research Association, vol. 8(2), pages 53-71, September.
    5. Brännäs, Kurt, 2003. "Temporal Aggregation of the Returns of a Stock Index Series," Umeå Economic Studies 614, Umeå University, Department of Economics.
    6. Kurt Brannas & Niklas Nordman, 2003. "An alternative conditional asymmetry specification for stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 537-541.
    7. Kulp-Tåg, Sofie, 2007. "Short-Horizon Asymmetric Mean-Reversion and Overreactions: Evidence from the Nordic Stock Markets," Working Papers 524, Hanken School of Economics.
    8. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004.
    9. Brännäs Kurt & De Gooijer Jan G. & Lönnbark Carl & Soultanaeva Albina, 2012. "Simultaneity and Asymmetry of Returns and Volatilities: The Emerging Baltic States' Stock Exchanges," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-24, January.
    10. Kurt Brannas & Albina Soultanaeva, 2011. "Influence of news from Moscow and New York on returns and risks of Baltic States’ stock markets," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 11(1), pages 109-124, July.
    11. Brännäs, Kurt & Soultanaeva, Albina, 2006. "Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices," Umeå Economic Studies 696, Umeå University, Department of Economics.
    12. María José Rodríguez & Esther Ruiz, 2012. "Revisiting Several Popular GARCH Models with Leverage Effect: Differences and Similarities," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 10(4), pages 637-668, September.
    13. repec:spr:stmapp:v:11:y:2002:i:2:d:10.1007_bf02511487 is not listed on IDEAS

    More about this item

    Keywords

    Time series; finance; nonlinearity; estimation; testing; forecasting; NYSE;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:umnees:0535. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (David Skog). General contact details of provider: http://edirc.repec.org/data/inumuse.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.